The Phoenix Deep Survey: The Clustering and Environment of Extremely Red Objects
Bibliographic record
Abstract
In this paper we explore the clustering properties and environment of the extremely red objects (EROs; I - K > 4 mag) detected in a ≈180 arcmin 2 deep ( K s ≈ 20 mag) K s -band survey of a region within the Phoenix Deep Survey, an ongoing multiwavelength program aiming to investigate the nature and evolution of faint radio sources. Using our complete sample of 289 EROs brighter than K s = 20 mag, we estimate a statistically significant (≈3.7 σ) angular correlation function signal with amplitude A w = 8.7 × 10 -3 (assuming w (θ) = A w θ -0.8 , with θ in degrees), consistent with earlier work based on smaller samples. This amplitude suggests a clustering length in the range r o = 12-17 h -1 Mpc, implying that EROs trace regions of enhanced density. Using a novel method, we further explore the association of EROs with galaxy overdensities by smoothing the K -band galaxy distribution using the matched filter algorithm of Postman et al. (1996) and then cross-correlating the resulting density maps with the ERO positions. Our analysis provides direct evidence that EROs are associated with overdensities at redshifts z ≳ 1. We also exploit the available deep radio 1.4 GHz data (limiting flux 60 μJy) to explore the association of EROs and faint radio sources and whether the two populations trace similar large-scale structures. Cross-correlation of the two samples (after excluding 17 EROs with radio counterparts) gives a 2 σ signal only for the subsample of high- z radio sources ( z > 0.6). Although the statistics are poor, this suggests that it is the high- z radio subsample that traces similar structures with EROs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".